基于扩展卡尔曼滤波的卫星组合姿态确定物理仿真技术研究

针对未来卫星应用的高精度、高可靠性定姿技术需求,设计了卫星单轴组合姿态确定物理仿真系统;采用太阳敏感器和光纤陀螺组合定姿模式,提出了一种扩展卡尔曼滤波组合姿态确定算法,用C语言进行了实验软件设计,系统采用DSP为处理器,还设计了多敏感器数据采集扩展接口;物理仿真实验结果:姿态确定精度优于0.5°;陀螺常值漂移估计的平均值为3.625758e-4°/s,证明了组合姿态确定算法的有效性和该项技术对工程应用的可行性。     

超声作用下刚性壁面附近的双气泡脉动

在实际应用时,空化泡可能位于刚性壁附近。对刚性壁附近的空化泡脉动进行研究有利于更好地利用声空化。文章研究了刚性壁附近双气泡的动力学规律。研究结果表明,当两气泡与刚性壁距离相同时,气泡与壁之间的距离越大,刚性壁对辐射声波的反射越小,气泡脉动时能够达到的最大半径与最小半径的比值(即压缩比)也越大。若改变单个气泡与刚性壁的距离,则当两个气泡距离接近时,位置固定的气泡压缩比会减小。增大单个气泡的平衡半径,会使得两气泡脉动时的压缩比变小。此外,文章还对两气泡间距固定情况下,气泡压缩比与两气泡中心连线和壁面所成夹角之间的关系进行了讨论。     

宽翼T形梁桥动力学理论与特性分析

考虑了剪滞翘曲应力自平衡条件、剪切变形和剪力滞后效应等因素的影响,本文提出了一种对宽翼薄壁T形梁动力学特性的分析方法.分析中为了准确反应T形梁翼板的动位移变化,三个广义动位移被引入,且以能量变分原理为基础建立了T形梁动力反应的控制微分方程和自然边界条件,据此对T形梁的动力反应特性进行了分析,揭示了T形梁桥动力反应的规律.算例中,对比了考虑和不考虑剪滞翘曲应力自平衡条件对T形梁动力反应的影响,结果显示考虑剪滞翘曲应力自平衡条件的计算方法与有限元数值解吻合更好.     

不完全观测数据下混合效应模型的正交投影估计

基于矩阵的QR分解技术,对一类含有不完全观测数据的线性混合效应模型提出了一种基于正交投影的估计方法。在一些正则条件下,证明了固定效应参数的估计渐近服从标准正态分布,得到了固定效应参数的置信区间。另外,所提出的固定效应参数的估计过程不受随机效应的任何影响,具有较好的有效性和稳健性。最后,通过一些数值模拟和一个实例分析研究了所提出估计方法的有限样本性质。     

Modification of Iron with Degradable Silver Phases Processed via Laser Beam Melting for Implants with Adapted Degradation Rate

In medical technology, implants are used to improve the quality of patients’ lives. The development of materials with adapted properties can further increase the benefit of implants. If implants are only needed temporarily, biodegradable materials are beneficial. In this context, iron-based materials are promising due to their biocompatibility and mechanical properties, but the degradation rate needs to be accelerated. Apart from alloying, the creation of noble phases to cause anodic dissolution of the iron-based matrix is promising. Due to its high electrochemical potential, immiscibility with iron, biocompatibility, and antibacterial properties, silver is suited for the creation of such phases. A suitable technology for processing immiscible material combinations is powder-bed-based procedure like laser beam melting. This procedure offers short exposure times to high temperatures and therefore a limited time for diffusion of alloying elements. As the silver phases remain after the dissolution of the iron matrix, a modification is needed to ensure their degradability. Following this strategy, pure iron with 5 wt% of a degradable silver–calcium–lanthanum alloy is processed via laser beam melting. Investigation of the microstructure yields achievement of the intended microstructure and long-term degradation tests indicates an impact on the degradation, but no increased degradation rate.     

Modeling and Analysis of UAV-Assisted Mobile Network with Imperfect Beam Alignment

With the rapid development of emerging 5G and beyond (B5G), Unmanned Aerial Vehicles (UAVs) are increasingly important to improve the performance of dense cellular networks. As a conventional metric, coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous environment. In recent years, stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network systems. In this paper, an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been proposed. An assumption was considered that all users are distributed according to Poisson Cluster Process (PCP) around base stations, in particular, Thomas Cluster Process (TCP). Using this model, the impact of beam alignment errors on the coverage probability was investigated. Initially, the Probability Density Function (PDF) of directional antenna gain between the user and its serving base station was obtained. Then, association probability with each tier was achieved. A tractable expression was derived for coverage probability in both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) condition links. Numerical results demonstrated that at low UAVs altitude, beam alignment errors significantly degrade coverage performance. Moreover, for a small cluster size, alignment errors do not necessarily affect the coverage performance.     

Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment. The data were collected to verify the performance of the proposed models for wearable devices. Finally, the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58% and 99.16%, respectively, for the four strokes. The accuracy for five-fold cross validation was 99.87%. This result also shows that the multi-scale convolutional neural network has better robustness after five-fold cross validation.     

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around the globe. Nonetheless, this is not eminently efficacious considering human inspection of medical images can yield a high false positive rate. Ineffective and inefficient diagnosis is a crucial reason for such a high mortality rate for this malady. However, the conspicuous advancements in deep learning and artificial intelligence have stimulated the development of exceedingly precise diagnosis systems. The development and performance of these systems rely prominently on the data that is used to train these systems. A standard problem witnessed in publicly available medical image datasets is the severe imbalance of data between different classes. This grave imbalance of data can make a deep learning model biased towards the dominant class and unable to generalize. This study aims to present an end-to-end convolutional neural network that can accurately differentiate lung nodules from non-nodules and reduce the false positive rate to a bare minimum. To tackle the problem of data imbalance, we oversampled the data by transforming available images in the minority class. The average false positive rate in the proposed method is a mere 1.5 percent. However, the average false negative rate is 31.76 percent. The proposed neural network has 68.66 percent sensitivity and 98.42 percent specificity.     

压电谐振驱动三足机器人的平面运动

设计并实现了一类利用压电陶瓷片作动,由三条曲梁足支撑的振动驱动机器人.建立了在一条足共振驱动下机器人水平运动的动力学方程,数值计算解释了摩擦作用下的运动机理,寻找到异性摩擦对运动方向、速度的影响和压电激励频率与运动速度间的关系.通过建立圆弧曲梁控制方程求解圆弧型足面内振动的固有频率及振型,设计了三组不同频率的圆弧曲梁足参数,实验制作了机器人模型,利用压电控制三足间振动的共振切换,实现了预想的三个方向的运动以达到平面运动的效果,实验测量了机器人的运动速度与理论计算吻合得较好.     

Experimental deformation analysis of an adhesively bonded multi-material joint for marine applications

The strength and deformation of full-scale adhesively bonded multi-material joints is studied in this paper. Four joints with a thick layer of methyl methacrylate adhesive (MMA) have been manufactured in shipyard conditions. In two specimens, cracks have been introduced at steel–adhesive and composite–adhesive interfaces. One cracked and one un-cracked specimen were subjected to quasi-static tensile testing; the two remaining specimens were stepwise loaded/unloaded with increasing load until failure. The strain in the adhesive layers was measured with digital image correlation (DIC). This showed a predominant shear deformation and dissimilar shear strain patterns for different bond lines. Fibre Bragg (FBG) sensors were used to monitor strains at steel and composite constituents and to detect the onset and evolution of damage in the un-cracked specimen. Strains measured by FBG sensors correspond well with DIC results at nearby regions. All specimens failed by delamination of the composite panel near the composite–adhesive interface.